
"""simple docstring"""



import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
 from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm


def 			_UpperCamelCase						(    _A   )     ->	Optional[Any]:





    """simple docstring"""

    if is_torch_version("""<"""       ,   """2.0.0"""   ) or not hasattr(_A       ,   """_dynamo"""   ):
        return False
    return isinstance(_A       ,   torch._dynamo.eval_frame.OptimizedModule   )


def 			_UpperCamelCase						(    _A       ,   _A = True   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

    _UpperCAmelCase        = is_compiled_module(_A   )
    if is_compiled:
        _UpperCAmelCase        = model
        _UpperCAmelCase        = model._orig_mod

    if is_deepspeed_available():
        options += (DeepSpeedEngine,)

    while isinstance(_A       ,   _A   ):
        _UpperCAmelCase        = model.module

    if not keep_fpaa_wrapper:
        _UpperCAmelCase        = getattr(_A       ,   """forward"""   )
        _UpperCAmelCase        = model.__dict__.pop("""_original_forward"""       ,   _A   )
        if original_forward is not None:
            while hasattr(_A       ,   """__wrapped__"""   ):
                _UpperCAmelCase        = forward.__wrapped__
                if forward == original_forward:
                    break
            _UpperCAmelCase        = forward
        if getattr(_A       ,   """_converted_to_transformer_engine"""       ,   _A   ):
            convert_model(_A       ,   to_transformer_engine=_A   )

    if is_compiled:
        _UpperCAmelCase        = model
        _UpperCAmelCase        = compiled_model

    return model


def 			_UpperCamelCase						(    )     ->	int:





    """simple docstring"""

    PartialState().wait_for_everyone()


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    if PartialState().distributed_type == DistributedType.TPU:
        xm.save(_A       ,   _A   )
    elif PartialState().local_process_index == 0:
        torch.save(_A       ,   _A   )


@contextmanager
def 			_UpperCamelCase						(    **_A   )     ->	int:





    """simple docstring"""

    for key, value in kwargs.items():
        _UpperCAmelCase        = str(_A   )

    yield

    for key in kwargs:
        if key.upper() in os.environ:
            del os.environ[key.upper()]


def 			_UpperCamelCase						(    _A   )     ->	List[str]:





    """simple docstring"""

    if not hasattr(_A       ,   """__qualname__"""   ) and not hasattr(_A       ,   """__name__"""   ):
        _UpperCAmelCase        = getattr(_A       ,   """__class__"""       ,   _A   )
    if hasattr(_A       ,   """__qualname__"""   ):
        return obj.__qualname__
    if hasattr(_A       ,   """__name__"""   ):
        return obj.__name__
    return str(_A   )


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    for key, value in source.items():
        if isinstance(_A       ,   _A   ):
            _UpperCAmelCase        = destination.setdefault(_A       ,   {}   )
            merge_dicts(_A       ,   _A   )
        else:
            _UpperCAmelCase        = value

    return destination







def 			_UpperCamelCase						(    _A = None   )     ->	bool:





    """simple docstring"""

    if port is None:
        _UpperCAmelCase        = 2_9_5_0_0
    with socket.socket(socket.AF_INET       ,   socket.SOCK_STREAM   ) as s:
        return s.connect_ex(("""localhost""", port)   ) == 0

"""simple docstring"""



import cva
import numpy as np




class 		a_    :







    def __init__(      self      :							Optional[Any]  ,       __UpperCamelCase      :							float  ,       __UpperCamelCase      :							int  )		->Dict:






        '''simple docstring'''






        if k in (0.0_4, 0.0_6):
            _UpperCAmelCase        = k
            _UpperCAmelCase        = window_size
        else:
            raise ValueError("""invalid k value"""  )







    def __str__(      self      :							Tuple  )		->str:






        '''simple docstring'''






        return str(self.k  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  )		->tuple[cva.Mat, list[list[int]]]:






        '''simple docstring'''






        _UpperCAmelCase        = cva.imread(__UpperCamelCase  ,       0  )
        _UpperCAmelCase     ,_UpperCAmelCase        = img.shape
        _UpperCAmelCase        = []
        _UpperCAmelCase        = img.copy()
        _UpperCAmelCase        = cva.cvtColor(__UpperCamelCase  ,       cva.COLOR_GRAY2RGB  )
        _UpperCAmelCase     ,_UpperCAmelCase        = np.gradient(__UpperCamelCase  )
        _UpperCAmelCase        = dx**2
        _UpperCAmelCase        = dy**2
        _UpperCAmelCase        = dx * dy
        _UpperCAmelCase        = 0.0_4
        _UpperCAmelCase        = self.window_size // 2
        for y in range(__UpperCamelCase  ,       h - offset  ):
            for x in range(__UpperCamelCase  ,       w - offset  ):
                _UpperCAmelCase        = ixx[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = iyy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()
                _UpperCAmelCase        = ixy[
                    y - offset : y + offset + 1, x - offset : x + offset + 1
                ].sum()

                _UpperCAmelCase        = (wxx * wyy) - (wxy**2)
                _UpperCAmelCase        = wxx + wyy
                _UpperCAmelCase        = det - k * (trace**2)
                # Can change the value
                if r > 0.5:
                    corner_list.append([x, y, r]  )
                    color_img.itemset((y, x, 0)  ,       0  )
                    color_img.itemset((y, x, 1)  ,       0  )
                    color_img.itemset((y, x, 2)  ,       2_55  )
        return color_img, corner_list


if __name__ == "__main__":
 a       :						List[Any]       		=		HarrisCorner(0.04, 3)
 a		,			a       :						List[Any]       		=		edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)